车辆路径问题（影印版）

　　list of contributors
　　preface
　　1 an overview of vehicle routing problems
　　1.1 introduction
　　1.2 problem definition and basic notation
　　1.2.1 capacitated and distance-constrained vrp
　　1.2.2 vrp with time windows
　　1.2.3 vrp with backhauls
　　1.2.4 vrp with pickup and delivery
　　1.3 basic models for the vrp
　　1.3.1 vehicle flow models
　　1.3.2 extensions of vehicle flow models
　　1.3.3 commodity flow models
　　1.3.4 set-partitioning models
　　1.4 test instances for the cvrp and other vrps
　　bibliography
　　ⅰ capacitated vehicle routing problem
　　2 branch-and-bound algorithms for the capacitated vrp
　　2.1 introduction
　　2.2 basic relaxations
　　2.2.1 bounds based on assignment and matching
　　2.2.2 bounds based on arborescences and trees
　　2.2.3 comparison of the basic relaxations
　　2.3 better relaxations
　　2.3.1 additive bounds for acvrp
　　2.3.2 further lower bounds for acvrp
　　2.3.3 lagrangian lower bounds for scvrp
　　2.3.4 lower bounds from a set-partitioning formulation
　　2.3.5 comparison of the improved lower bounds
　　2.4 structure of the branch-and-bound algorithms for cvrp
　　2.4.1 branching schemes and search strategies
　　2.4.2 reduction, dominance rules, and other features
　　2.4.3 performance of the branch-and-bound algorithms
　　2.5 distance-constrained vrp
　　bibliography
　　3 branch-and-cut algorithms for the capacitated vrp
　　3.1 introduction and two-index flow model
　　3.2 branch-and-cut
　　3.3 polyhedral studies
　　3.3.1 capacity constraints
　　3.3.2 generalized capacity constraints
　　3.3.3 framed capacity constraints
　　3.3.4 valid inequalities from stsp
　　?　　3.3.5 valid inequalities combining bin packing and stsp
　　3.3.6 valid inequalities from the stable set problem
　　3.4 separation procedures
　　3.4.1 exact separation of capacity constraints
　　3.4.2 heuristics for capacity and related constraints
　　3.4.3 stsp constraints
　　3.5 branching strategies
　　3.6 computational results
　　3.7 conclusions
　　bibliography
　　4 set-covering-based algorithms for the capacitated vrp
　　4.1 introduction
　　4.2 solving the linear programming relaxation of p
　　4.3 set-covering-based solution methods
　　4.3.1 branch-and-bound algorithm for problem cg
　　4.3.2 polyhedral branch-and-bound algorithm
　　4.3.3 pseudo-polynomial lower bound on cmin
　　4.3.4 solving pa via dual-ascent and branch-and-bound
　　4.4 solving the set-covering integer program
　　4.4.1 a cutting plane method
　　4.4.2 branch-and-price
　　4.4.3 additional comments on computational approaches
　　4.5 computational results
　　4.6 effectiveness of the set-covering formulation
　　4.6.1 worst-case analysis
　　4.6.2 average-case analysis
　　bibliography
　　5 classical heuristics for the capacitated vrp
　　5.1 introduction
　　5.2 constructive methods
　　5.2.1 clarke and wright savings algorithm
　　5.2.2 enhancements of the clarke and wright algorithm
　　5.2.3 matching-based savings algorithms
　　5.2.4 sequential insertion heuristics
　　5.3 two-phase methods
　　5.3.1 elementary clustering methods
　　5.3.2 truncated branch-and-bound
　　5.3.3 petal algorithms
　　5.3.4 route-first, cluster-second methods
　　5.4 improvement heuristics
　　5.4.1 single-route improvements
　　5.4.2 multiroute improvements
　　5.5 conclusions
　　bibliography
　　6 metaheuristics for the capacitated vrp
　　6.1 introduction
　　6.2 simulated annealing
　　6.2.1 two early simulated annealing algorithms
　　6.2.2 osman's simulated annealing algorithms
　　6.2.3 van breedam's experiments
　　6.3 deterministic annealing
　　6.4 tabu search
　　6.4.1 two early tabu search algorithms
　　6.4.2 osman's tabu search algorithm
　　6.4.3 taburoute
　　6.4.4 taillard's algorithm
　　6.4.5 xu and kelly's algorithm
　　6.4.6 rego and roucairol's algorithms
　　6.4.7 barbarosoglu and ozgur's algorithm
　　6.4.8 adaptive memory procedure of rochat and taillard
　　6.4.9 granular tabu search of toth and vigo
　　6.4.10 computational comparison
　　6.5 genetic algorithms
　　6.5.1 simple genetic algorithm
　　6.5.2 application to sequencing problems
　　6.5.3 application to the vrp
　　6.6 ant algorithms
　　6.7 neural networks
　　6.8 conclusions
　　bibliography
　　ⅱ important variants of the vehicle routing problem
　　7 vrp with time windows
　　7.1 introduction
　　7.2 problem formulation
　　7.2.1 formulation
　　7.2.2 network lower bound
　　7.2.3 linear programming lower bound
　　7.2.4 algorithms
　　7.3 upper bounds: heuristic approaches
　　7.3.1 route construction
　　7.3.2 route improvement
　　?　7.3.3 composite heuristics
　　7.3.4 metaheuristics
　　7.3.5 parallel implementations
　　7.3.6 optimization-based heuristics
　　7.3.7 asymptotically optimal heuristics
　　7.4 lower bounds from decomposition approaches
　　7.4.1 lagrangian relaxation
　　7.4.2 capacity and time-constrained shortest-path problem.
　　7.4.3 variable splitting
　　7.4.4 column generation
　　7.4.5 set-partitioning formulation
　　7.4.6 lower bound
　　7.4.7 commodity aggregation
　　7.4.8 hybrid approach
　　7.5 integer solutions
　　7.5.1 binary decisions on arc flow variables
　　7.5.2 integer decisions on arc flow variables
　　7.5.3 binary decisions on path flow variables
　　7.5.4 subtour elimination and 2-path cuts
　　7.5.5 k-path cuts and parallelism
　　7.5.6 integer decisions on (fractional and integer) time variables
　　7.6 special cases
　　7.6.1 multiple tsp with time windows
　　7.6.2 vrp with backhauls and time windows
　　7.7 extensions
　　7.7.1 heterogeneous fleet, multiple-depot, and initial conditions
　　7.7.2 fleet size
　　7.7.3 multiple time windows
　　7.7.4 soft time windows
　　7.7.5 time- and load-dependent costs
　　7.7.6 driver considerations
　　7.8 computational results for vrptw.
　　7.9 conclusions
　　bibliography
　　8 vrp with backhauls
　　8.1 introduction
　　8.1.1 benchmark instances
　　8.2 integer linear programming models
　　8.2.1 formulation of toth and vigo
　　8.2.2 formulation of mingozzi, giorgi, and baldacci
　　8.3 relaxations
　　8.3.1 relaxation obtained by dropping the cccs
　　8.3.2 relaxation based on projection
　　8.3.3 lagrangian relaxation
　　8.3.4 overall additive lower bound
　　8.3.5 relaxation based on the set-partitioning model
　　8.4 exact algorithms
　　8.4. i algorithm of toth and vigo
　　8.4.2 algorithm of mingozzi, giorgi, and baldacci
　　8.4.3 computational results for the exact algorithms
　　8.5 heuristic algorithms
　　8.5.1 algorithm of deif and bodin
　　8.5.2 algorithms of goetschalckx and jacobs-blecha
　　8.5.3 algorithm of toth and vigo
　　8.5.4 computational results for the heuristics
　　bibliography
　　9 vrp with pickup and delivery
　　9.1 introduction
　　9.2 mathematical formulation
　　9.2.1 construction of the networks
　　9.2.2 formulation
　　9.2.3 service quality
　　9.2.4 reduction of the network size
　　9.3 heuristics
　　9.3.1 construction and improvement
　　9.3.2 clustering algorithms
　　9.3.3 metaheuristics
　　9.3.4 neural network heuristics
　　9.3.5 theoretical analysis of algorithms
　　9.4 optimization-based approaches
　　9.4.1 single vehicle cases
　　9.4.2 multiple vehicle cases
　　9.5 applications
　　9.6 computational results
　　9.7 conclusions
　　bibliography
　　ⅲ applications and case studies
　　10 routing vehicles in the real world: applications in the solid waste,
　　10.1 introduction
　　10.2 computerized vehicle routing in the solid waste industry
　　10.2.1 history
　　10.2.2 background
　　10.2.3 commercial collection
　　10.2.4 residential collection
　　10.2.5 case studies
　　10.2.6 roll-on-roll-off
　　10.2.7 further remarks
　　10.3 vehicle routing in the beverage, food, and dairy industries
　　10.3.1 introduction
　　10.3.2 beverage industry
　　10.3.3 food industry
　　10.3.4 dairy industry
　　10.4 distribution and routing in the newspaper industry
　　10.4.1 industry background
　　10.4.2 newspaper distribution problem
　　10.4.3 vehicle routing algorithms for ndp
　　10.4.4 three case studies
　　10.4.5 further remarks
　　10.5 conclusions
　　bibliography
　　11 capacitated arc routing problem with vehicle-site dependencies: the philadelphia experience
　　11.1 introduction
　　11.2 networks, assumptions, and goals of the carp-vsd
　　11.2.1 travel network
　　11.2.2 service network
　　11.2.3 vehicle classes
　　11.2.4 travel network and service network for a vehicle class
　　11.2.5 vehicle preference list
　　11.2.6 other assumptions
　　11.2.7 goals and constraints of the carp-vsd
　　11.3 vehicle decomposition algorithm (vda)
　　11.3.1 step a. create and verify vehicle class networks
　　11.3.2 step b. estimate total work and determine initial fleet mix
　　11.3.3 step c. partition the service network
　　11.3.4 step d. determine travel path and balance the partitions
　　11.3.5 step e. revise estimate of total work and adjust fleet mix
　　11.4 implementation of the vda in philadelphia
　　11.5 enhancements and extensions
　　bibliography
　　12 inventory routing in practice
　　12.1 introduction
　　12.2 problem definition
　　12.3 literature review
　　12.4 solution approach
　　12.4.1 phase i: integer programming model
　　12.4.2 phase i: solving the integer programming model
　　12.4.3 phase ii: scheduling
　　12.5 computational experience
　　12.5.1 instances
　　12.5.2 solution quality
　　12.5.3 alternate heuristic
　　12.5.4 computational experiments
　　12.6 conclusion
　　bibliography
　　13 routing under uncertainty: an application in the scheduling of field service engineers
　　13.1 introduction
　　13.2 vrpsst with variable costs of recourse
　　13.3 literature review
　　13.3.1 vrpst
　　13.3.2 vrpsd
　　13.4 stochastic integer vrpsst formulation
　　13.4.1 first-stage problem
　　13.4.2 second-stage problem
　　13.5 paired tree search algorithm (ptsa)
　　13.5.1 linked trees
　　13.5.2 lower bounds
　　13.5.3 computational implementation
　　13.6 applied maintenance scheduling problem
　　13.6.1 maintenance scheduling system in practice
　　13.6.2 stochastic problem setting
　　13.7 modeling the applied problem as a vrpsst
　　13.8 model input
　　13.8.1 job locations and the road network
　　13.8.2 service times
　　13.9 model output: computational considerations
　　13.9.1 framework for the analysis of results
　　13.9.2 reoptimization
　　13.10 example scenario
　　13.11 overall computational results
　　13.12 conclusion
　　bibliography
　　14 evolution of microcomputer-based vehicle routing software: case studies in the united states
　　14.1 introduction
　　14.2 definition of the vrp
　　14.2.1 customer specification
　　14.2.2 product specification
　　14.2.3 vehicle specification
　　14.3 algorithms
　　14.4 future trends in vehicle routing software
　　14.5 summary and conclusions
　　bibliography
　　index

　　    作者：（美国）托夫（Paolo Toth） （美国）Daniele Vigo
　　    Paolo Toth is a Professor of Combinatorial Optimization at the Faculty of Engineering of the University of Bologna. His current research interests concern the design of algorithms for combinatorial optimization and graph theory problems and their application in real-world transportation, crew management and routing and loading problems. In July 1998, he was awarded the Euro Gold Medal. He has published more than 90 papers internationally,has co-authored and edited several books, and serves as editor for several journals. He is president of the International Federation of the Operational Research Societies (IFORS} for the period 2001-2003.
　　    Daniele Vigo is an Associate Professor of Operations Research at the Faculty of Engineering of the University of Bologna. His main research activities are in the field of combinatorial optimization, with particular interest in the design of algorithms for routing, cutting, packing, and crew management problems. He has published more than 30 papers internationally and serves as Associate Editor for the journal Operations Research. 

　　in the field of combinatorial optimization problems, the vehicle routing problem (vrp) is one of the most challenging. defined more than 40 years ago, the problem involves designing the optimal set of routes for fleets of vehicles for the purpose of serving a given set of customers . interest in vrp is motivated by its practical relevance as well as its considerable difficulty.
　　the vehicle routing problem covers both exact and heuristic methods developed for the vrp and some of its main variants, emphasizing the practical issues common to vrp. the book is composed of three parts containing contributions from well-known experts. the first part covers basic vrp, known more commonly as capacitated vrp. the second part covers three main variants of vrp: with time windows, backhauls, and pickup and delivery. the third part covers issues arising in real-world vrp applications and includes both case studies and references to software packages.
　　this book will be of interest to both researchers and graduate-level students in the communities of operations research and mathematical sciences. it focuses on a specific family of problems while offering a complete overview of the effective use of the most important techniques proposed for the solution of hard combinatorial problems. practitioners will find this book particularly useful.
　　reader need a basic knowledge of the main methods for the solution of combinatorial optimization problems.